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Imports

library(stargazer)

Warning: package 'stargazer' was built under R version 4.1.2

Please cite as: 

 Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.

 R package version 5.2.3. https://CRAN.R-project.org/package=stargazer 

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✔ ggplot2 3.3.5     ✔ purrr   0.3.4
✔ tibble  3.1.5     ✔ dplyr   1.0.7
✔ tidyr   1.1.4     ✔ stringr 1.4.0
✔ readr   2.0.2     ✔ forcats 0.5.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(patchwork)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

load("data/models.RData")

wahl_lohn <- readRDS("data/wahl_lohn_mod.rds")

Plots

Basic plot with SB

plot_basic <- ggplot(aes(x = lohn_prozent, y = afd_prozent, label=name), data = wahl_lohn) +
  geom_point(shape = 1) +
  geom_smooth(method = "lm", se = T) +
  labs(
    x = "Anteil Mindestlohnbezieher",
    y = "AfD Stimmen in %",
    title = "AfD Stimmen und Mindestlohnbezieher",
    subtitle = "Kreisebene",
    caption = "Daten: WSI, Bundeswahlleiter"
  ) +
  theme_minimal() #+ #scientific theme
  #coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 0.35)) + #start at 0,0
  # for Sonneberg (16072)
  # geom_point(
  #   data = wahl_lohn %>% filter("kreis" == "16072"),
  #   color = "red",
  # ) + 
  # geom_text(
  #   data = wahl_lohn %>% filter("kreis" == "16072"),
  #   label = "SB",
  #   vjust = 1.3,
  #   hjust = 0,
  #   color = "red"
  # )
ggsave("images/plot_basic.png", plot_basic, width = 10, height = 7)

`geom_smooth()` using formula 'y ~ x'

plotly::ggplotly(plot_basic)

`geom_smooth()` using formula 'y ~ x'

Plot with east germany

ggplot(aes(x = lohn_prozent, y = afd_prozent), data = wahl_lohn) +
  geom_point(aes(color = factor(ost))) +
  geom_smooth(method = "lm") +
  labs(
    x = "Anteil Mindestlohnbezieher",
    y = "AfD Stimmen in %",
    title = "AfD Stimmen und Mindestlohnbezieher",
    subtitle = "Kreisebene",
    caption = "Daten: WSI, Bundeswahlleiter"
  ) +
  scale_color_manual(values = c("red", "blue"), name="Osten") + #color of points
  theme_minimal() + #scientific theme
  coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 0.35))  #start at 0,0

`geom_smooth()` using formula 'y ~ x'

Plot with scale for arbeitslosenquote

ggplot(aes(x = lohn_prozent, y = afd_prozent), data = wahl_lohn) +
  geom_point(aes(color = arbeitslosenquote)) +
  geom_smooth(method = "lm") +
  labs(
    x = "Anteil Mindestlohnbezieher",
    y = "AfD Stimmen in %",
    title = "AfD Stimmen und Mindestlohnbezieher",
    subtitle = "Kreisebene",
    caption = "Daten: WSI, Bundeswahlleiter"
  ) +
  scale_color_viridis_c(name = "Arbeitslosenquote", option="magma", direction=-1) + #color of points
  theme_minimal() + #scientific theme
  coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 0.35))  #start at 0,0

`geom_smooth()` using formula 'y ~ x'

Plot for linkspartei

ggplot(aes(x = lohn_prozent, y = linke_prozent), data = wahl_lohn) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_minimal() #scientific theme

`geom_smooth()` using formula 'y ~ x'

Warning: Removed 1 rows containing non-finite values (stat_smooth).

Warning: Removed 1 rows containing missing values (geom_point).

Maps

Load GEOJSON Data for Kreise

Data Source = Regionalatlas Statistikportal

spdf <- sf::read_sf("data/kreise.geojson")

Merge

geodata <- spdf %>% 
  left_join(wahl_lohn, by = c("schluessel" = "kreis"))

Afd Stimmen

afd_map <- ggplot(geodata) +
  geom_sf(aes(fill = afd_prozent)) +
  scale_fill_viridis_c(name = "AfD in %", option="magma", direction=-1) +
  theme_minimal() +
  labs(
    title = "AfD Vote Share",
    subtitle = "District Level",
    caption = "Data: Bundeswahlleiter"
  )

Mindestlohn

lohn_map <- ggplot(geodata) +
  geom_sf(aes(fill = lohn_prozent)) +
  scale_fill_viridis_c(name = "MW %", option="magma", direction=-1) +
  theme_minimal() +
  labs(
    title = "Minimum Wage Recipients",
    subtitle = "District Level",
    caption = "Data: WSI"
  )

both maps next to each other

maps <- afd_map + lohn_map
ggsave("images/maps.png",plot=maps, width = 10, height = 5)

subplot(ggplotly(afd_map), ggplotly(lohn_map))

Regression Models Table

Summary Statistic of Data

stargazer(as.data.frame(wahl_lohn), type="text")

============================================================
Statistic          N     Mean     St. Dev.   Min      Max   
------------------------------------------------------------
afd_prozent       400   0.113      0.058    0.029    0.321  
lohn_prozent      400   0.194      0.064    0.079    0.440  
linke_prozent     399   0.045      0.029    0.015    0.155  
ost               400   0.190      0.393      0        1    
arbeitslosenquote 400   0.052      0.022    0.019    0.148  
gdp               400 40,329.500 16,743.150 17,553  158,749 
age               400   45.277     2.008    40.700  51.000  
pop               400  536.540    708.669   35.300 4,788.200
foreigners        400   0.114      0.055    0.025    0.375  
------------------------------------------------------------

Compile List of Models to include in Table

models <- list(
  model_basic,
  model_ost,
  model_arbeit,
  model_gdp,
  model_age,
  model_foreign,
  model_pop
)

Create the Stargazer Table

table <- stargazer(models, 
          #type = "text",
          type = "html",
          out="data/table.html", #
          title = "Effect of Minimum Wage Recipients on AfD Vote Share",
          dep.var.labels = "AfD Vote Share",
          covariate.labels = c(
            "Minimum Wage Rate", 
            "East Germany",
            "Unemployment Rate", 
            "Log GDP p. C.", 
            "Avg. Age", 
            "Log Pop. Density", 
            "Foreigners"
            ),
          df = T,
          omit.stat = c("rsq", "f")
          )

Effect of Minimum Wage Recipients on AfD Vote Share

	Dependent variable:
	AfD Vote Share
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Minimum Wage Rate	0.714***	0.291***	0.308***	0.301***	0.227***	0.222***	0.202***
	(0.028)	(0.039)	(0.039)	(0.044)	(0.044)	(0.043)	(0.043)
East Germany		0.086***	0.088***	0.088***	0.081***	0.088***	0.091***
		(0.006)	(0.006)	(0.006)	(0.006)	(0.006)	(0.006)
Unemployment Rate			-0.279***	-0.275***	-0.244***	-0.383***	-0.222**
			(0.071)	(0.072)	(0.070)	(0.078)	(0.092)
Log GDP p. C.				-0.002	0.011*	0.002	0.006
				(0.005)	(0.006)	(0.006)	(0.006)
Avg. Age					0.006***	0.008***	0.008***
					(0.001)	(0.001)	(0.001)
Log Pop. Density						0.175***	0.243***
						(0.046)	(0.050)
Foreigners							-0.008***
							(0.002)
Constant	-0.026***	0.040***	0.051***	0.071	-0.332***	-0.351***	-0.315***
	(0.006)	(0.007)	(0.007)	(0.060)	(0.091)	(0.090)	(0.090)
Observations	400	400	400	400	400	400	400
Adjusted R2	0.612	0.732	0.742	0.741	0.760	0.768	0.773
Residual Std. Error	0.036 (df = 398)	0.030 (df = 397)	0.030 (df = 396)	0.030 (df = 395)	0.029 (df = 394)	0.028 (df = 393)	0.028 (df = 392)
Note:	p<0.1; p<0.05; p<0.01

Rewrite this code with table package gt